The expansion over the last decade of observations in Australia with 1-min temporal resolution allows an assessment of temperature variations over very short periods, using data from 75 stations ...between 2012 and 2020. The mean difference between the highest and lowest temperatures within 1 min is greatest in the middle of the day, and greater in summer than winter at most locations, except in the northern tropics where it peaks towards the end of the dry season in spring. At noon in summer, the mean 1-min difference exceeds 0.4°C at numerous locations in semi-arid and arid regions, but is near 0.2°C at a range of southern and coastal locations. At night, it is between 0.05 and 0.10°C, with little seasonal variation, at most locations, although slightly higher in some areas subject to local topographically forced influences such as katabatic winds. There is evidence at some locations of daytime 1-min variations being larger (smaller) when antecedent conditions are abnormally dry (wet), indicating a possible role for the land surface in amplifying or dampening short-period temperature variations. In addition to any inherent interest, these results have applications in data quality control, and in assessing the confidence level that can be applied to estimated maximum and minimum temperatures on days with some missing observations.
Indices for climate variability and extremes have been used for a long time, often by assessing days with temperature or precipitation observations above or below specific physically‐based ...thresholds. While these indices provided insight into local conditions, few physically based thresholds have relevance in all parts of the world. Therefore, indices of extremes evolved over time and now often focus on relative thresholds that describe features in the tails of the distributions of meteorological variables. In order to help understand how extremes are changing globally, a subset of the wide range of possible indices is now being coordinated internationally which allows the results of studies from different parts of the world to fit together seamlessly. This paper reviews these as well as other indices of extremes and documents the obstacles to robustly calculating and analyzing indices and the methods developed to overcome these obstacles. Gridding indices are necessary in order to compare observations with climate model output. However, gridding indices from daily data are not always straightforward because averaging daily information from many stations tends to dampen gridded extremes. The paper describes recent progress in attribution of the changes in gridded indices of extremes that demonstrates human influence on the probability of extremes. The paper also describes model projections of the future and wraps up with a discussion of ongoing efforts to refine indices of extremes as they are being readied to contribute to the IPCC's Fifth Assessment Report. WIREs Clim Change 2011, 2:851–870. doi: 10.1002/wcc.147
This article is categorized under:
Paleoclimates and Current Trends > Modern Climate Change
Daily Peak Wind Gust (DPWG) time series are important for the evaluation of wind‐related hazard risks to different socioeconomic and environmental sectors. Yet, wind time series analyses can be ...impacted by several artefacts, both temporally and spatially, which may introduce inhomogeneities that mislead the study of their decadal variability and trends. The aim of this study is to present a strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time series across Australia for 1941–2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.1 of the R package Climatol. This approach is an advance in homogenization of climate records as it identifies 353 break points based on monthly data, splits the daily series into homogeneous subperiods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (a) automatically homogenize a large number of DPWG series, including short‐term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (b) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (c) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data were also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of this approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long‐term variability of extreme winds across Australia.
An approach to automatically homogenize daily peak wind gusts is presented. Detection of break points cannot be based solely on metadata or reanalysis data. The first homogenized daily peak wind gust (DPWG) dataset across Australia for 1941–2016 is developed.
We explore the causes and predictability of extreme low minimum temperatures (T
min
) that occurred across northern and eastern Australia in September 2019. Historically, reduced T
min
is related to ...the occurrence of a positive Indian Ocean Dipole (IOD) and central Pacific El Niño. Positive IOD events tend to locate an anomalous anticyclone over the Great Australian Bight, therefore inducing cold advection across eastern Australia. Positive IOD and central Pacific El Niño also reduce cloud cover over northern and eastern Australia, thus enhancing radiative cooling at night-time. During September 2019, the IOD and central Pacific El Niño were strongly positive, and so the observed T
min
anomalies are well reconstructed based on their historical relationships with the IOD and central Pacific El Niño. This implies that September 2019 T
min
anomalies should have been predictable at least 1–2 months in advance. However, even at zero lead time the Bureau of Metereorolgy ACCESS-S1 seasonal prediction model failed to predict the anomalous anticyclone in the Bight and the cold anomalies in the east. Analysis of hindcasts for 1990–2012 indicates that the model's teleconnections from the IOD are systematically weaker than the observed, which likely stems from mean state biases in sea surface temperature and rainfall in the tropical Indian and western Pacific Oceans. Together with this weak IOD teleconnection, forecasts for earlier-than-observed onset of the negative Southern Annular Mode following the strong polar stratospheric warming that occurred in late August 2019 may have contributed to the T
min
forecast bust over Australia for September 2019.
The austral spring climate of 2020 was characterised by the occurrence of La Niña, which is the most predictable climate driver of Australian springtime rainfall. Consistent with this La Niña, the ...Bureau of Meteorology's dynamical sub-seasonal to seasonal forecast system, ACCESS-S1, made highly confident predictions of wetter-than-normal conditions over central and eastern Australia for spring when initialised in July 2020 and thereafter. However, many areas of Australia received near average to severely below average rainfall, particularly during November. Possible causes of the deviation of rainfall from its historical response to La Niña and causes of the forecast error are explored with observational and reanalysis data for the period 1979-2020 and real-time forecasts of ACCESS-S1 initialised in July to November 2020. Several compounding factors were identified as key contributors to the drier-than-anticipated spring conditions. Although the ocean surface to the north of Australia was warmer than normal, which would have acted to promote rainfall over northern Australia, it was not as warm as expected from its historical relationship with La Niña and its long-term warming trend. Moreover, a negative phase of the Indian Ocean Dipole mode, which typically acts to increase spring rainfall in southern Australia, decayed earlier than normal in October. Finally, the Madden-Julian Oscillation activity over the equatorial Indian Ocean acted to suppress rainfall across northern and eastern Australia during November. While ACCESS-S1 accurately predicted the strength of La Niña over the Niño3.4 region, it over-predicted the ocean warming to the north of Australia and under-predicted the strength of the November MJO event, leading to an over-prediction of the Australian spring rainfall and especially the November-mean rainfall.
A new version of the long‐term Australian temperature data set, known as ACORN‐SAT (Australian Climate Observations Reference Network—Surface Air Temperature), has been developed. ACORN‐SAT includes ...homogenized daily maximum and minimum temperature data from 112 locations across Australia, encompassing the period from 1910 to the present, with 60 of the locations having data for the full 1910–2018 period. Homogenization is achieved using a percentile‐matching methodology with a number of improvements beyond practices used in previous versions, including more effective detection and removal of potentially inhomogeneous reference stations and an enhanced breakpoint detection methodology. Explicit corrections have also been introduced for a change in instrument screen size, whilst an assessment has found that the transition from manual to automatic instruments and changes in effective response time of automatic instruments have had a negligible impact on the data. Adjustments associated with documented site moves from in‐town to out‐of‐town locations are predominantly negative, particularly for minimum temperature, with other adjustments showing no strong bias towards either positive or negative values. The new data set shows slightly stronger warming (0.12°C per decade in mean temperature over the 1910–2016 period) than either the previous ACORN‐SAT version (0.10°C) or the unhomogenized gridded data (0.08°C), primarily due to more effective treatment of systematic moves of sites out of towns and the removal of a rounding bias in the version 1 methodology.
A new version of the long‐term Australian temperature data set, known as ACORN‐SAT, has been developed. ACORN‐SAT includes homogenized daily maximum and minimum temperature data from 112 locations across Australia (including the site shown, at Rutherglen), from 1910 to the present. The new data set shows slightly stronger warming (0.12°C per decade in mean temperature over the 1910–2016 period) than the previous ACORN‐SAT version (0.10°C) or the unhomogenized gridded data (0.08°C).
This summary looks at the southern hemisphere and equatorial climate patterns for spring 2016, with particular attention given to the Australasian and equatorial regions of the Pacific and Indian ...Ocean basins. Spring 2016 was marked by the later part of a strong negative phase of the Indian Ocean Dipole, alongside cool neutral El Niño–Southern Oscillation conditions. September was exceptionally wet over much of Australia, contributing to a wet spring with near-average temperatures. The spring was one of the warmest on record over the southern hemisphere as a whole, with Antarctic Sea ice extent dropping to record low levels for the season.
Observed global mean surface temperature (GMST) combines a forced response with internal variability, and quantifying internal variability is important in assessing the reaching of key thresholds, ...such as the 1.5°C warming threshold in the Paris Agreement. This study uses observational data to estimate internal variability. Since the current period of warming began in the 1970s, the 10‐year mean of GMST has been very close to the 30‐year mean for the period it is centred in and can therefore be considered as a robust indicator of the recent state of the climate. The range between the 5th and 95th percentile of annual residuals of observed GMST is 0.319°C, substantially less than the corresponding range of 0.4°C–0.6°C in large model ensembles, implying that the first individual year above 1.5°C may occur later than indicated by climate models. The largest annual residuals are mostly associated with large‐amplitude El Niño‐Southern Oscillation (ENSO) events or major volcanic eruptions, with the relationship between cool years and La Niña more consistent than that between warm years and El Niño. The relationship between multi‐year GMST means for differing periods indicates that the probability that the 1.5°C threshold has been crossed (using the IPCC definition of the midpoint of the first 20‐year period above the threshold) exceeds 50% once the most recent observed 11‐year mean reaches 1.43°C.
Plain Language Summary
An increase in temperature of 1.5°C from pre‐industrial times is widely seen as a critical point in climate change, but how do we know when warming of 1.5°C has occurred? “Crossing” of 1.5°C is formally defined using 20‐year averages, but since we don't want to wait many years to know whether we've crossed 1.5°C or not, how can we make use of the data we have now? This study shows how to use existing observations to determine how likely it is that 1.5°C has been crossed. It also shows how much global temperatures vary from year to year, and when we might expect to start seeing individual years above 1.5°C (which many people will interpret as meaning that there has been a sustained crossing of 1.5°C). Global temperatures do not vary as much from year to year in observations as they do in models, which suggests that the first years above 1.5°C may happen a little later than forecasts based on models say they will.
Key Points
Observed interannual variability of global mean surface temperature is less than simulated in large model ensembles
Observed variability can be used alongside recent data to estimate the probability that key temperature thresholds have been crossed
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